Projects sharing features with Parca

This project is a comprehensive technical guide and diagnostic manual for analyzing memory, performance, and asynchronous behavior within Node.js applications. It provides detailed methods for asynchronous tracing, memory diagnostics, and performance analysis to resolve runtime errors and execution bottlenecks. The resource distinguishes itself by covering advanced diagnostic workflows, including the use of flame graphs for CPU profiling, the capture and comparison of heap snapshots for memory leak detection, and the mapping of asynchronous call stacks. It also provides technical guidance on

SkyWalking is an application performance monitoring system and observability platform designed to collect and analyze metrics, traces, and logs from distributed microservices. It functions as a distributed tracing platform and a telemetry data pipeline that ingests and aggregates observability data from various language agents. The project features an AI-powered anomaly detector that uses machine learning to calculate metric baselines and identify irregular URI patterns. It includes an eBPF performance profiler for diagnosing CPU and network bottlenecks at the kernel level and generates inter

Pixie is an open-source observability platform for Kubernetes that uses eBPF to automatically capture telemetry data from clusters without requiring any manual instrumentation or code changes. It functions as an eBPF telemetry collector, a continuous application profiler, a network traffic analyzer, and a scriptable telemetry query engine, all within a single Kubernetes-native tool. The platform distinguishes itself through several integrated capabilities. It continuously samples stack traces from compiled-language code to identify CPU performance bottlenecks, visualizing the results as inter

go-torch is a profiling tool for capturing the execution state of Go programs and transforming raw binary data into visual representations of program performance. It functions as a flame graph profiler and performance visualization utility that identifies expensive code paths through the collection of CPU and memory stack traces. The tool features a network-capable remote process profiler that connects to endpoints to capture and export execution profiles from Go binaries. It utilizes stochastic profiling to synthesize execution data into call graphs, allowing for the identification of bottle

PerfView is a set of integrated profiling and tracing tools built on Windows Event Tracing for Windows (ETW), designed to diagnose CPU, memory, and ETW-based performance issues in .NET applications. It captures ETW events to analyze runtime behavior, CPU usage, and memory allocations, serving as a .NET performance profiler that measures both CPU time and garbage collection events. The tool distinguishes itself with a diff comparison engine that compares two performance traces side-by-side to highlight changes in method costs and event rates. It renders profiled call stacks as flame graphs whe

Async-profiler is a suite of performance tools designed for sampling Java runtimes, tracking memory allocations, and monitoring hardware counters. It functions as a low-overhead sampling profiler for Java applications, collecting stack traces and memory allocation data without safepoint bias. The project provides specialized utilities for generating interactive flame graphs to visualize execution hotspots in a web browser. It includes a hardware performance counter monitor to track low-level system events such as cache misses and page faults. The toolset covers several diagnostic domains, in

Kubeshark is a network observability platform designed for Kubernetes environments, functioning as an eBPF-powered engine for cluster-wide traffic analysis. It captures, indexes, and visualizes network activity and API calls directly from the kernel, providing deep visibility into service-to-service communication without requiring sidecar proxies or manual code instrumentation. The platform distinguishes itself through its ability to perform protocol-aware traffic dissection and user-space cryptographic hooking, which allows for the inspection of encrypted traffic and the reconstruction of ap

FlameGraph is a performance profiling and visualization toolkit designed to identify bottlenecks in software execution. It functions as a processing engine that transforms raw stack trace samples into interactive, hierarchical diagrams. By representing aggregated execution frequency as nested rectangles, the tool allows developers to visualize hot code paths and analyze system behavior across both kernel and user-space environments. The project distinguishes itself through its ability to perform differential profile analysis, which highlights performance regressions or improvements by compari

Memray is a memory profiler for Python that tracks heap allocations in both Python code and native C or C++ extensions. It captures memory events by hooking into the language runtime and traversing call stacks, providing a comprehensive view of how an application consumes memory. The tool is designed to minimize performance impact on the target application by using thread-local buffering and streaming data to an external process or file. The project distinguishes itself through its ability to monitor complex, multi-threaded systems and child processes in real-time. It provides diagnostic util

backward-cpp is a C++ stack trace library and debugging utility designed to capture, resolve, and print detailed execution traces and crash reports. It functions as a crash reporter and call stack printer that translates raw memory addresses into human-readable function names, filenames, and line numbers. The project provides automated crash reporting by registering system handlers for fatal errors, such as segmentation faults, to automatically generate execution traces upon program failure. It distinguishes itself by extracting source file fragments from disk to display specific lines of cod

MemProcFS is a volatile memory analysis tool and cross-platform memory acquisition system. It functions as a memory forensic virtual file system, mapping physical memory and kernel objects into a virtual directory structure that allows users to analyze system artifacts using standard file system tools. The project distinguishes itself by providing a virtual file system for memory forensics, enabling the browsing and querying of physical memory as read-only files and folders. It also incorporates a Yara-based memory scanner to identify malware signatures and injected code within physical memor

ServerStatus is an infrastructure observability platform and multi-server monitoring dashboard. It utilizes a system of remote agent-based metrics collectors to gather server telemetry and push that data to a central server via HTTP. The platform provides a centralized web interface for tracking the real-time health, resource usage, and availability of multiple remote servers. It includes a threshold-based alerting system that triggers notifications when server resource metrics exceed predefined limits or watchdogs. The system covers cloud infrastructure observability through remote resource

Falcon Plus is an infrastructure monitoring platform designed to aggregate performance metrics from distributed agents and manage infrastructure health alerts. It functions as a containerized service orchestrator and a distributed metrics aggregator, providing a centralized system for routing monitoring data and exposing health statistics via a REST observability interface. The platform features a dedicated system alert manager that evaluates incoming performance data against predefined thresholds to trigger operational alarms. It employs a data pipeline to consolidate real-time metrics from

Komari is a self-hosted server monitoring system and cross-platform agent used to track system resource metrics and health across Linux, Windows, and Android servers. It provides a centralized dashboard for observing server performance and managing infrastructure privately. The project features a web-based remote terminal that utilizes bidirectional connections to execute commands and manage remote servers through a shell interface. It also includes a network connectivity monitor for measuring reachability and latency via ICMP, TCP, and HTTP requests, as well as a traffic usage tracker to mon

BCC is an eBPF development toolkit and tracing framework used for monitoring and analyzing the Linux kernel. It functions as a performance analysis tool and debugging utility to capture system events, measure kernel latency, and provide network observability. The project distinguishes itself by providing a build system that integrates with LLVM to compile C-like code into BPF bytecode at runtime. It utilizes BPF Type Format data for relocations to maintain cross-kernel compatibility and extracts kernel headers to ensure the generated programs match the specific kernel version. The toolkit co

Bloaty is a binary size profiler and executable format analyzer that attributes every byte of a binary to specific symbols or compile units. It functions as a compilation unit profiler and C++ symbol demangler, mapping binary size back to source files and translating mangled names into human-readable formats. The project distinguishes itself as a binary diffing tool capable of comparing two versions of a binary to isolate specific symbols or sections responsible for size regressions. It provides specialized support for recovering symbols in stripped binaries by associating them with external

Scrutiny is a distributed hardware monitoring system and predictive drive failure analyzer. It provides a centralized management platform and web-based dashboard for tracking hard drive health and S.M.A.R.T. metrics across multiple remote servers. The system functions as a S.M.A.R.T. alerting gateway and storage health trend visualizer. It estimates hardware risk by comparing drive attributes against real-world failure thresholds and records historical data to identify gradual degradation patterns that may not trigger immediate alerts. Capabilities include distributed data collection via rem

The diagnostics project provides a cross-platform diagnostic infrastructure and command-line toolkit for monitoring runtime performance, analyzing memory dumps, and troubleshooting applications. It features a custom inter-process communication protocol for command and telemetry exchange across platforms, a low-overhead event pipe mechanism for streaming real-time diagnostic events and performance counters from running processes, and automated remote symbol resolution for stack trace analysis. The platform includes native debugger extensions that integrate with standard debuggers to inspect ma

Hotspot is a graphical user interface for analyzing and visualizing performance data captured by the Linux perf tool. It functions as a performance profiling visualizer and assembly-level profiler that maps performance costs to specific instructions synchronized with original source code. The project distinguishes itself through a remote symbol resolver that maps performance data from embedded targets to local host debug symbols and sysroots. It also includes a specialized off-CPU analysis tool designed to identify thread wait times and I/O blocks using kernel scheduler tracepoints. The tool

Monitor is a centralized infrastructure monitoring platform designed to collect, visualize, and analyze performance metrics across distributed server environments. It utilizes an agent-based architecture to gather both standard and custom performance data, which is then stored and processed to provide real-time insights into system health and resource utilization. The platform distinguishes itself through automated operational workflows, including dynamic service discovery that registers new agents without manual intervention and hierarchical alert escalation that routes notifications based o

LibreNMS is an SNMP network monitoring system and IT infrastructure management suite. It serves as an automated network discovery tool and infrastructure dashboard, enabling the identification and monitoring of network hardware and operating systems. The system differentiates itself through a rule-based alerting engine and a comprehensive IT incident workflow integration. It supports complex alert routing, including escalation sequences and direct ticket generation for project management and service desk platforms. Its observability capabilities cover multi-vendor hardware oversight, applica

HyperDbg is a hardware-assisted kernel-mode debugging platform that leverages virtualization to monitor and control system execution. By utilizing hypervisor-level primitives, it enables deep system analysis and instrumentation without relying on standard operating system debugging interfaces. The framework provides a comprehensive environment for inspecting both kernel and user-mode processes, allowing for granular control over execution flow and system state. The project distinguishes itself through a transparent debugging layer designed to remain invisible to the target environment. It emp

Orbit is a set of specialized tools for C and C++ performance profiling, binary symbol mapping, and remote process and thread analysis. It provides a system for analyzing execution time and resource usage, utilizing a call graph visualizer to map function entries and exits into hierarchical execution flows for individual threads. The project distinguishes itself with a remote process profiler capable of capturing performance data from applications running on remote hosts. It also includes a thread scheduling analyzer that tracks context switches and processor core utilization to visualize thr

Async-profiler is a sampling profiler for Java applications that tracks CPU time and stack traces across execution frames to identify performance bottlenecks. It is designed to capture profiling data without introducing timing bias. The project provides capabilities for JVM memory analysis to locate native and heap allocation hotspots and memory leaks. It also includes system contention analysis to identify resource bottlenecks through the tracking of contended locks and hardware performance counters. The tool converts raw profiling data into visual performance reports, including interactive

Lit is a machine learning interpretability framework and model debugging tool designed to analyze model behavior and performance. It serves as an interpretability dashboard for large language models and a general performance analyzer for text, image, and tabular datasets. The project distinguishes itself through a comprehensive suite of interpretability tools, including salience map generation for feature attribution, the creation of synthetic and counterfactual examples to test robustness, and the projection of high-dimensional embeddings into visual spaces via UMAP or PCA. It further enable

Detect-It-Easy is a binary file identifier and analysis toolkit designed to determine file formats, compilers, and packers. It functions as a binary file identifier that utilizes signature matching and heuristic analysis to identify executable and archive formats. The project includes a custom file signature engine and a scriptable rule system for defining and applying detection logic to identify specific binary patterns. It features specialized detectors for Android packages, such as APK and DEX files, and a malware packer detector to identify protections, obfuscators, and virus families. T

This project is a containerized orchestration layer for the Elastic Stack, providing a pre-configured set of Docker Compose files to deploy Elasticsearch, Logstash, and Kibana as a unified data analysis stack. It functions as a centralized log management system for ingesting, indexing, and searching log data using a cluster of interconnected services. The deployment pattern includes an Elasticsearch cluster manager that enables scaling data nodes through replica scaling and internal discovery. It provides a web-based administration interface for monitoring cluster health and status. The syst